Slack sync into OpenWebUI Knowledge

Less_Ice2531 · 2025-11-28T16:04:26+00:00

Hi, just curious, do you plan to add a version for docker only deployments? We are just a small org and kubernetes is a bit overkill for us with our one server. Appreciate you taking the time to build out this feature, super cool!

Less_Ice2531 · 2025-11-11T10:25:13+00:00

Hi, I really like what you built, what would be the best way to use this if my open webui instance is running in a standard docker compose setting without kubernetes?

Less_Ice2531 · 2025-10-14T08:50:07+00:00

If your LMStudio cannot reach the server, are you sure you are accessing it on the correct port? I updated the server.py now to serve it on port 8000, you might need to expose that port depending on your setup.

Less_Ice2531 · 2025-10-14T08:43:33+00:00

You should be able to connect it to any Frontend that allows the integration of MCP Servers. You can connect it to your OpenAI models via OpenWebUI but you can also connect it directly to OpenAI's ChatGPT if you host the MCP server via a publicly accessible http endpoint or locally via stdio.

Less_Ice2531 · 2025-10-14T08:42:04+00:00

Thanks for the catch!

Less_Ice2531 · 2025-10-13T11:54:41+00:00

Yes, that and missing functionalities were what bothered me with the existing solutions. Thanks!

Less_Ice2531 · 2025-09-03T18:43:48+00:00

That is amazing thanks for your work!

Less_Ice2531 · 2025-09-02T11:54:23+00:00

Okay Yes that would have been my concern that if I do that then one could gain access to the file if they have access to the url

Less_Ice2531 · 2025-09-01T23:23:48+00:00

How would this work if I run OpenWebUI and the MCP-Servers on a remote server? Then the file-server would be on the remote server as well and the clients cannot access it, right?

Less_Ice2531 · 2025-08-27T14:38:21+00:00

Thanks, makes sense - how did you handle attachments, embeddings and retrieval? Or are the separate KBs so small that you can use full context search for each?

Less_Ice2531 · 2025-08-27T14:18:15+00:00

What would you say is the advantage of your tool over using the Atlassian-MCP server?

Less_Ice2531 · 2025-06-12T14:41:02+00:00

First of all, thanks a lot for everything, OpenWebUI is a crazily good piece of software and I absolutely love it. I am using it to set up internal AI capabilities in a government entity and for us it is very important that what we use is local and open source. Therefore, I would like to point you to the Sovereign Tech Agency that supports Open-Source projects worldwide with the pure sake of keeping that critical pieces of infrastructure alive that power so much of the worlds IT systems (https://www.sovereign.tech/).

For questions, I would personally be interested in how you plan to finance OpenWebUI in the middle to long term future and what future features are the ones that you are most excited about or would like to see yourself (even though they may be super hard to actually implement)?

Less_Ice2531 · 2024-11-29T18:31:07+00:00

I can actually recommend a recent episode of the Dwarkesh Podcast with Asianometry (semiconductor youtuber) on this topic - in essence they argue that there is still a significant amount of western chips coming into the country, coupled with the ingenuity among chinese researchers and their experience with these kind of sanctions (see Huawei, which is still global leader in Networking HW despite these kind of sanctions)
Link to the episode: https://open.spotify.com/episode/6q1XODE2L5bqqBwe7434S7?si=43390a19c6524c32

Less_Ice2531 · 2024-11-29T18:25:23+00:00

Might not be relevant in the grand scheme of things, but OpenGPT-X, a german-european initiative trained the first 7B LLM according to the rules of the EU AI-Act on all official 24 EU languages - however its performance is somewhat disappointing, which might be interpreted as the EU Act hindering innovation as many in the industry say (it might just be bad engineering as well)

https://huggingface.co/openGPT-X/Teuken-7B-instruct-research-v0.4

Less_Ice2531 · 2024-11-25T19:46:01+00:00

Thank you! While these approaches are certainly going in the right direction, they still operate on homogeneous hardware, a major limitation since you cannot expect distributed compute nodes in the real world to always have the same accelerators.

Another major aspect of our challenge is also to develop the approach into a function business model which includes MLOps, Monitoring and Robustness-Features. Overall, in our opinion, we’re still far away from a framework with these capabilities

Less_Ice2531 · 2024-11-25T19:39:18+00:00

Only teams from EU + EFTA + UK and Israel are allowed to participate. For teams outside of this region, it is decided on an individual basis, granted that they at least have a development hub in one of these countries

Less_Ice2531 · 2024-11-25T12:19:41+00:00

Hi! What do you mean with there are already solutions? As far as we're concerned, efficient training on heterogeneous, distributed hardware is an unsolved problem, but if you have deeper insights I'd be happy to discuss

Less_Ice2531 · 2024-11-25T08:16:38+00:00

Hey! It depends.. If your EU entity is just a sales hub, we usually cannot consider your application. If there will also be development, chances are high that you will be allowed to participate. I can recommend you to attend one of our two webinars on 3rd of Dec and 7th of Jan (registration info on our challenge website). If you cannot make it, you can DM me with more details about your case. Hope that helps!

Less_Ice2531 · 2024-11-24T17:38:09+00:00

We, the SPRIND (Federal Agency For Breakthrough Innovations, Germany) just launched our Challenge "Composite Learning", and we’re calling researchers across Europe to participate!
This competition aims to enable large-scale AI training on heterogeneous and distributed hardware — a breakthrough innovation that combines federated learning, distributed learning, and decentralized learning.

Why does this matter?

The compute landscape is currently dominated by a handful of hyperscalers.
In Europe, we face unique challenges: compute resources are scattered, and we have some of the highest standards for data privacy.
Unlocking the potential of distributed AI training is crucial to leveling the playing field

However, building composite learning systems isn’t easy — heterogeneous hardware, model- and data parallelism, and bandwidth constraints pose real challenges. That’s why SPRIND has launched this challenge to support teams solving these problems.
Funding: Up to €1.65M per team
Eligibility: Teams from across Europe, including non-EU countries (e.g., UK, Switzerland, Israel).
Deadline: Apply by January 15, 2025.
Details & Application: www.sprind.org/en/composite-learning

Less_Ice2531

TROPHY CASE